Structured Matrix Approximations via Tensor Decompositions

Misha Kilmer develops structured matrix approximation by an invertible matrix-to-tensor transformation, tensor approximation, and a mapping back to matrix space. Different tensor decompositions yield sums of structured Kronecker products, block low-rank matrices, or combinations of both. The framework exposes latent operator structure useful for large computations, and the talk considers where randomization could help. Joint work with Arvind Saibaba at North Carolina State University.